In [59]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from collections import Counter
import ast
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.metrics import mean_squared_error, r2_score
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.model_selection import GridSearchCV
In [2]:
# Load the dataset
file_path = '/Users/shaunmckellarjr/Desktop/Shaun_Projects/Movie_Recommender/movies2.csv'
movies_df = pd.read_csv(file_path)
In [3]:
movies_df.head()
Out[3]:
| id | original_title | overview | release_date | popularity | vote_average | vote_count | genre_names | |
|---|---|---|---|---|---|---|---|---|
| 0 | 278 | The Shawshank Redemption | Imprisoned in the 1940s for the double murder ... | 1994-09-23 | 116.038 | 8.705 | 26221 | ['Drama', 'Crime'] |
| 1 | 238 | The Godfather | Spanning the years 1945 to 1955, a chronicle o... | 1972-03-14 | 167.834 | 8.695 | 19877 | ['Drama', 'Crime'] |
| 2 | 240 | The Godfather Part II | In the continuing saga of the Corleone crime f... | 1974-12-20 | 164.908 | 8.576 | 12001 | ['Drama', 'Crime'] |
| 3 | 424 | Schindler's List | The true story of how businessman Oskar Schind... | 1993-12-15 | 77.039 | 8.567 | 15409 | ['Drama', 'History', 'War'] |
| 4 | 389 | 12 Angry Men | The defense and the prosecution have rested an... | 1957-04-10 | 76.566 | 8.541 | 8276 | ['Drama'] |
In [4]:
missing_values = movies_df.isnull().sum()
In [5]:
print("\nMissing Values in Each Column:")
print(missing_values)
Missing Values in Each Column: id 0 original_title 0 overview 1 release_date 0 popularity 0 vote_average 0 vote_count 0 genre_names 0 dtype: int64
In [6]:
data_types = movies_df.dtypes
print("nData Types of Each Column:")
print(data_types)
nData Types of Each Column: id int64 original_title object overview object release_date object popularity float64 vote_average float64 vote_count int64 genre_names object dtype: object
In [7]:
duplicates = movies_df.duplicated().sum()
print("\nNumber of Duplicate Rows")
print (duplicates)
Number of Duplicate Rows 48
In [8]:
summary_statistics = movies_df.describe()
print("\nSummary Stats of Numerical Columns:")
print(summary_statistics)
Summary Stats of Numerical Columns:
id popularity vote_average vote_count
count 9.400000e+03 9400.000000 9400.000000 9400.000000
mean 1.886253e+05 42.855597 6.636562 1971.110319
std 2.501381e+05 90.352175 0.782143 3092.303425
min 2.000000e+00 0.020000 3.270000 300.000000
25% 1.004650e+04 16.841750 6.114750 466.000000
50% 3.665250e+04 24.360500 6.658000 842.000000
75% 3.440978e+05 38.226000 7.200000 1966.000000
max 1.212073e+06 3629.389000 8.705000 35768.000000
In [9]:
movies_df['release_date']= pd.to_datetime(movies_df['release_date'],
errors='coerce')
movies_df.drop_duplicates(inplace=True)
In [10]:
movies_df.drop_duplicates(inplace=True)
In [11]:
print("\nCleaned Data info:")
print(movies_df.info)
Cleaned Data info:
<bound method DataFrame.info of id original_title \
0 278 The Shawshank Redemption
1 238 The Godfather
2 240 The Godfather Part II
3 424 Schindler's List
4 389 12 Angry Men
... ... ...
9395 9544 FearDotCom
9396 485774 The Open House
9397 10870 Les Dalton
9398 74849 The Star Wars Holiday Special
9399 5491 Battlefield Earth
overview release_date \
0 Imprisoned in the 1940s for the double murder ... 1994-09-23
1 Spanning the years 1945 to 1955, a chronicle o... 1972-03-14
2 In the continuing saga of the Corleone crime f... 1974-12-20
3 The true story of how businessman Oskar Schind... 1993-12-15
4 The defense and the prosecution have rested an... 1957-04-10
... ... ...
9395 When four bodies are discovered among the indu... 2002-08-09
9396 A teenager and his mother find themselves besi... 2018-01-19
9397 Joe and Averell are the eldest and youngest of... 2004-11-08
9398 Luke Skywalker and Han Solo battle evil Imperi... 1978-11-17
9399 In the year 3000, man is no match for the Psyc... 2000-05-12
popularity vote_average vote_count \
0 116.038 8.705 26221
1 167.834 8.695 19877
2 164.908 8.576 12001
3 77.039 8.567 15409
4 76.566 8.541 8276
... ... ... ...
9395 14.975 3.724 346
9396 14.647 3.701 1317
9397 21.351 3.681 329
9398 11.516 3.300 441
9399 22.800 3.270 814
genre_names
0 ['Drama', 'Crime']
1 ['Drama', 'Crime']
2 ['Drama', 'Crime']
3 ['Drama', 'History', 'War']
4 ['Drama']
... ...
9395 ['Horror', 'Thriller', 'Crime']
9396 ['Horror', 'Thriller']
9397 ['Comedy', 'Western', 'Adventure']
9398 ['Fantasy', 'Science Fiction', 'Family', 'TV M...
9399 ['Science Fiction', 'Action', 'Adventure']
[9352 rows x 8 columns]>
Explatory Data Analysis¶
In [13]:
# Flattening the list of genres and count occurrences
genre_list = movies_df['genre_names'].apply(ast.literal_eval).explode()
genre_counts = Counter(genre_list)
In [14]:
# Plotting the genre distribution
plt.figure(figsize=(14, 7))
sns.barplot(x=list(genre_counts.keys()), y=list(genre_counts.values()))
plt.xticks(rotation=90)
plt.title('Distribution of Movie Genres')
plt.xlabel('Genre')
plt.ylabel('Frequency')
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version. order = pd.unique(vector)
In [15]:
correlation_matrix = movies_df[['popularity', 'vote_average', 'vote_count']].corr()
In [16]:
plt.figure(figsize=(10, 6))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix of Numerical Columns')
plt.show()
Pair Plots¶
In [21]:
# Detecting outliers
popularity_threshold = movies_df['popularity'].quantile(0.99)
vote_count_threshold = movies_df['vote_count'].quantile(0.99)
outliers = movies_df[(movies_df['popularity'] > popularity_threshold) |
(movies_df['vote_count'] > vote_count_threshold)]
print(outliers[['original_title', 'popularity', 'vote_count', 'vote_average']])
original_title popularity vote_count vote_average 0 The Shawshank Redemption 116.038 26221 8.705 1 The Godfather 167.834 19877 8.695 7 The Dark Knight 95.154 31978 8.515 8 기생충 89.722 17573 8.509 9 The Green Mile 88.370 16851 8.500 ... ... ... ... ... 7733 xXx 465.010 4068 5.933 7789 Suicide Squad 253.650 20668 5.913 8153 Judge Dredd 486.121 2340 5.786 8503 Madame Web 482.394 1359 5.611 8895 Ghostbusters 571.572 6236 5.358 [171 rows x 4 columns]
Popularity vs. Rating Analysis¶
In [22]:
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='popularity', data=movies_df)
plt.title('Popularity vs. Average Rating')
plt.xlabel('Average Rating')
plt.ylabel('Popularity')
plt.show()
In [23]:
genres = ['Action', 'Drama', 'Comedy', 'Horror'] # Modify based on your dataset
for genre in genres:
genre_movies = movies_df[movies_df['genre_names'].str.contains(genre)]
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='popularity', data=genre_movies)
plt.title(f'Popularity vs. Average Rating for {genre} Movies')
plt.xlabel('Average Rating')
plt.ylabel('Popularity')
plt.show()
In [26]:
# Ensure release_date is in datetime format
movies_df['release_date'] = pd.to_datetime(movies_df['release_date'], errors='coerce')
In [27]:
# Extract year and calculate decade
movies_df['release_year'] = movies_df['release_date'].dt.year
movies_df['decade'] = (movies_df['release_year'] // 10) * 10
In [28]:
# List of decades and genres to analyze
decades = movies_df['decade'].dropna().unique()
genres = ['Action', 'Drama', 'Horror', 'Comedy']
In [29]:
# Plot popularity vs average rating for each genre by decade
for genre in genres:
for decade in decades:
decade_movies = movies_df[(movies_df['decade'] == decade) &
(movies_df['genre_names'].str.contains(genre, na=False))]
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='popularity', data=decade_movies)
plt.title(f'Popularity vs. Average Rating for {genre} Movies in the {decade}s')
plt.xlabel('Average Rating')
plt.ylabel('Popularity')
plt.show()
In [32]:
features = ['vote_average', 'vote_count']
X = movies_df[features]
y = movies_df['popularity']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
plt.scatter(y_test, y_pred)
plt.xlabel('Actual Popularity')
plt.ylabel('Predicted Popularity')
plt.title('Actual vs. Predicted Popularity')
plt.show()
In [36]:
# Log transformation for skewed numerical features
movies_df['log_popularity'] = np.log1p(movies_df['popularity'])
movies_df['log_vote_count'] = np.log1p(movies_df['vote_count'])
In [37]:
# One-hot encoding for genres
genres_encoded = movies_df['genre_names'].str.get_dummies(sep=',')
movies_df = pd.concat([movies_df, genres_encoded], axis=1)
In [38]:
# Distribution of Movie Popularity
plt.figure(figsize=(10, 6))
sns.histplot(movies_df['log_popularity'], bins=20, kde=True)
plt.title('Distribution of Movie Popularity (Log Transformed)')
plt.xlabel('Log Popularity')
plt.ylabel('Frequency')
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [40]:
# Correlation Matrix of Numerical Features
numerical_cols = movies_df.select_dtypes(include=['float64', 'int64'])
correlation_matrix = numerical_cols.corr()
plt.figure(figsize=(12, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', linewidths=0.5)
plt.title('Correlation Matrix of Numerical Features')
plt.show()
In [41]:
# Pair Plots for Selected Numerical Features
sns.pairplot(movies_df, vars=['log_popularity', 'vote_average', 'log_vote_count'], diag_kind='kde')
plt.suptitle('Pair Plots for Selected Numerical Features', y=1.02)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [42]:
# Detecting and analyzing outliers
popularity_threshold = movies_df['log_popularity'].quantile(0.99)
vote_count_threshold = movies_df['log_vote_count'].quantile(0.99)
outliers = movies_df[(movies_df['log_popularity'] > popularity_threshold) |
(movies_df['log_vote_count'] > vote_count_threshold)]
print(outliers[['original_title', 'popularity', 'vote_count', 'vote_average']])
original_title popularity vote_count vote_average 0 The Shawshank Redemption 116.038 26221 8.705 1 The Godfather 167.834 19877 8.695 7 The Dark Knight 95.154 31978 8.515 8 기생충 89.722 17573 8.509 9 The Green Mile 88.370 16851 8.500 ... ... ... ... ... 7733 xXx 465.010 4068 5.933 7789 Suicide Squad 253.650 20668 5.913 8153 Judge Dredd 486.121 2340 5.786 8503 Madame Web 482.394 1359 5.611 8895 Ghostbusters 571.572 6236 5.358 [171 rows x 4 columns]
In [43]:
# Popularity vs. Rating Analysis
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='log_popularity', data=movies_df)
plt.title('Popularity vs. Average Rating')
plt.xlabel('Average Rating')
plt.ylabel('Log Popularity')
plt.show()
In [44]:
# Popularity vs. Average Rating for Each Genre
genres = ['Action', 'Drama', 'Comedy', 'Horror']
for genre in genres:
genre_movies = movies_df[movies_df['genre_names'].str.contains(genre, na=False)]
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='log_popularity', data=genre_movies)
plt.title(f'Popularity vs. Average Rating for {genre} Movies')
plt.xlabel('Average Rating')
plt.ylabel('Log Popularity')
plt.show()
In [45]:
# Popularity vs. Average Rating by Decade for Each Genre
movies_df['decade'] = (movies_df['release_year'] // 10) * 10
decades = movies_df['decade'].dropna().unique()
for genre in genres:
for decade in decades:
decade_movies = movies_df[(movies_df['decade'] == decade) &
(movies_df['genre_names'].str.contains(genre, na=False))]
plt.figure(figsize=(10, 6))
sns.scatterplot(x='vote_average', y='log_popularity', data=decade_movies)
plt.title(f'Popularity vs. Average Rating for {genre} Movies in the {decade}s')
plt.xlabel('Average Rating')
plt.ylabel('Log Popularity')
plt.show()
In [47]:
# Extracting the year and month from 'release_date'
movies_df['release_year'] = movies_df['release_date'].dt.year
movies_df['release_month'] = movies_df['release_date'].dt.month
In [48]:
# Prepare Data for Modeling
features = ['vote_average', 'log_vote_count', 'release_year', 'release_month'] + list(genres_encoded.columns)
X = movies_df[features]
y = movies_df['log_popularity']
In [49]:
# Spliting the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In [50]:
#Training and evaluating my linear regression model
model = LinearRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
In [51]:
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'Linear Regression - Mean Squared Error: {mse}')
print(f'Linear Regression - R-squared: {r2}')
Linear Regression - Mean Squared Error: 0.33856165816262884 Linear Regression - R-squared: 0.39919426883952913
In [52]:
plt.figure(figsize=(8, 6))
plt.scatter(y_test, y_pred, alpha=0.5)
plt.xlabel('Actual Log Popularity')
plt.ylabel('Predicted Log Popularity')
plt.title('Linear Regression: Actual vs. Predicted Popularity')
plt.show()
In [53]:
# Training and evaluating my Random Forest model
rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)
y_pred_rf = rf_model.predict(X_test)
In [54]:
mse_rf = mean_squared_error(y_test, y_pred_rf)
r2_rf = r2_score(y_test, y_pred_rf)
print(f'Random Forest - Mean Squared Error: {mse_rf}')
print(f'Random Forest - R-squared: {r2_rf}')
Random Forest - Mean Squared Error: 0.2828412202559387 Random Forest - R-squared: 0.4980748054566724
In [55]:
plt.figure(figsize=(8, 6))
plt.scatter(y_test, y_pred_rf, alpha=0.5)
plt.xlabel('Actual Log Popularity')
plt.ylabel('Predicted Log Popularity')
plt.title('Random Forest: Actual vs. Predicted Popularity')
plt.show()
In [56]:
# Important features from Random Forest
feature_importances = pd.Series(rf_model.feature_importances_, index=X.columns)
feature_importances = feature_importances.sort_values()
In [57]:
plt.figure(figsize=(12, 8))
feature_importances.plot(kind='barh')
plt.title('Feature Importances from Random Forest')
plt.xlabel('Importance')
plt.ylabel('Features')
plt.gca().invert_yaxis()
plt.show()
Train Gradient Boosting Model for evaluation¶
In [60]:
gb_model = GradientBoostingRegressor(random_state=42)
gb_model.fit(X_train, y_train)
y_pred_gb = gb_model.predict(X_test)
In [61]:
mse_gb = mean_squared_error(y_test, y_pred_gb)
r2_gb = r2_score(y_test, y_pred_gb)
print(f'Gradient Boosting - Mean Squared Error: {mse_gb}')
print(f'Gradient Boosting - R-squared: {r2_gb}')
Gradient Boosting - Mean Squared Error: 0.2727896940236453 Gradient Boosting - R-squared: 0.5159120720864656
In [62]:
plt.figure(figsize=(8,6))
plt.scatter(y_test, y_pred_gb, alpha=0.5)
plt.xlabel('Actual Log Popularity')
plt.ylabel('Predicted Log Popularity')
plt.title('Gradient Boosting: Actual vs. Predicted Popularity')
plt.show()
In [63]:
# Hyperparameter tuning for my Random Forest Model using Grid Search
param_grid = {
'n_estimators': [100, 200, 300],
'max_depth': [10, 20, None],
'min_samples_split': [2, 5, 10]
}
grid_search = GridSearchCV(RandomForestRegressor(random_state=42), param_grid, cv=3, n_jobs=-1, scoring='r2')
grid_search.fit(X_train, y_train)
best_rf_model = grid_search.best_estimator_
In [64]:
# Evaluating the best Random Forest model
y_pred_best_rf = best_rf_model.predict(X_test)
mse_best_rf = mean_squared_error(y_test, y_pred_best_rf)
r2_best_rf = r2_score(y_test, y_pred_best_rf)
print(f'Best Random Forest - Mean Squared Error: {mse_best_rf}')
print(f'Best Random Forest - R-squared: {r2_best_rf}')
Best Random Forest - Mean Squared Error: 0.27574660546071766 Best Random Forest - R-squared: 0.5106647875960475
In [65]:
# Grouping by year and calculate the mean of popularity, vote count, and vote average
yearly_trends = movies_df.groupby('release_year').agg({
'popularity': 'mean',
'vote_count': 'mean',
'vote_average': 'mean'
}).reset_index()
In [66]:
# Displaying the first few rows
print(yearly_trends.head())
release_year popularity vote_count vote_average 0 1902 20.963 1706.0 7.927 1 1903 11.828 584.0 7.008 2 1915 13.484 504.0 6.000 3 1916 12.227 321.0 7.120 4 1918 10.943 310.0 7.300
In [67]:
# Plotting the average popularity by year
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_year', y='popularity', data=yearly_trends)
plt.title('Average Movie Popularity by Year')
plt.xlabel('Year')
plt.ylabel('Average Popularity')
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [68]:
# Plotting the average vote count by year
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_year', y='vote_count', data=yearly_trends)
plt.title('Average Movie Vote Count by Year')
plt.xlabel('Year')
plt.ylabel('Average Vote Count')
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [69]:
# Plotting the average vote average by year
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_year', y='vote_average', data=yearly_trends)
plt.title('Average Movie Rating by Year')
plt.xlabel('Year')
plt.ylabel('Average Rating')
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [70]:
# Grouping by month and calculate the mean of popularity
monthly_trends = movies_df.groupby('release_month').agg({
'popularity': 'mean',
'vote_count': 'mean',
'vote_average': 'mean'
}).reset_index()
In [71]:
# Displaying the first few rows
print(monthly_trends.head())
release_month popularity vote_count vote_average 0 1 35.041960 1355.470803 6.474354 1 2 38.614401 1712.469417 6.550097 2 3 49.651451 1877.763780 6.607441 3 4 49.836220 1789.608498 6.514387 4 5 54.767445 2632.265723 6.674934
In [72]:
# Plotting the average popularity by month
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_month', y='popularity', data=monthly_trends, marker='o')
plt.title('Average Movie Popularity by Month')
plt.xlabel('Month')
plt.ylabel('Average Popularity')
plt.xticks(range(1, 13))
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [73]:
# Plotting the average vote count by month
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_month', y='vote_count', data=monthly_trends, marker='o')
plt.title('Average Movie Vote Count by Month')
plt.xlabel('Month')
plt.ylabel('Average Vote Count')
plt.xticks(range(1, 13))
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [74]:
# Plotting the average vote average by month
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_month', y='vote_average', data=monthly_trends, marker='o')
plt.title('Average Movie Rating by Month')
plt.xlabel('Month')
plt.ylabel('Average Rating')
plt.xticks(range(1, 13))
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
In [75]:
# Select genres to analyze
genres = ['Action', 'Drama', 'Comedy', 'Horror']
In [78]:
# Plotting trends for each genre by year
for genre in genres:
genre_trends = movies_df[movies_df['genre_names'].str.contains(genre, na=False)]
genre_yearly_trends = genre_trends.groupby('release_year').agg({
'popularity': 'mean',
'vote_count': 'mean',
'vote_average': 'mean'
}).reset_index()
plt.figure(figsize=(14, 6))
sns.lineplot(x='release_year', y='popularity', data=genre_yearly_trends)
plt.title(f'Average Popularity of {genre} Movies by Year')
plt.xlabel('Year')
plt.ylabel('Average Popularity')
plt.grid(True)
plt.show()
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
/opt/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
with pd.option_context('mode.use_inf_as_na', True):
